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Our data annotation services for building better AI datasets
AI models depend on data that is relevant, consistent, and accurately labeled. We support the annotation lifecycle from strategy and guideline development through dataset preparation, annotation, quality review, and enrichment, adapting our approach to the data, model requirements, and business context of each project.
Data Annotation Strategy
We establish the annotation framework before labeling begins, defining the scope, data coverage, labeling objectives, and requirements for the project. We consider the intended AI application and the nature of the available data to determine what information needs to be captured, creating a clear foundation for subsequent annotation activities and supporting more focused project execution.
Annotation Guideline Development
We create detailed instructions that translate annotation requirements into practical labeling rules. Guidelines define how annotators should classify information, handle exceptions, distinguish similar categories, and address ambiguous cases. This gives annotation teams a consistent reference for interpreting data and making labeling decisions throughout the project across varied annotation scenarios.
Custom Data Annotation
We label data according to the requirements of the intended AI application, working with the categories, attributes, and structures defined for the project. Our annotation work can accommodate different data formats and labeling requirements, helping enterprises create datasets that capture the specific information their AI systems need for accurate model development and evaluation purposes.
Training Data Preparation
We prepare annotated and source data for incorporation into AI training and fine-tuning workflows. This includes organizing files, applying required formats, structuring labeled information, and preparing datasets for downstream use. The objective is to make annotation outputs easier for development teams to incorporate into their existing AI development and deployment workflows.
Annotation Ontology Design
We design the classification framework used when datasets contain multiple related concepts, categories, attributes, or entities. We establish how these elements relate to one another and how they should be represented within the annotation structure. This provides a more organized basis for labeling complex datasets with interconnected information across diverse enterprise data and application requirements.
Annotation Quality Management
We establish measurable quality controls for annotation projects, defining how quality should be monitored throughout production. Depending on requirements, this can include sampling rates, agreement thresholds, reviewer procedures, and quality metrics. These controls provide a systematic way to monitor annotation standards as teams process larger volumes of data across different annotation projects.
Human-in-the-Loop Annotation
We incorporate human expertise into annotation workflows where accurate labeling depends on contextual judgment or interpretation. Annotators can handle nuanced information, difficult classifications, and cases where automated approaches are unsuitable. This allows human judgment to remain central when the data cannot be reliably labeled through straightforward or fully automated processes.
Annotation Review & Validation
We inspect completed annotations to verify that labeled data meets the defined requirements. Reviews can focus on classification accuracy, missing labels, incorrect boundaries, structural issues, or deviations from established rules. Findings can then be addressed before datasets move into subsequent AI development activities, providing greater confidence in annotation outputs.
Dataset Enrichment
We update existing datasets as new information, categories, or application requirements emerge. This can involve adding newly available examples, extending existing labels, or incorporating additional categories into established datasets. Ongoing enrichment helps organizations expand dataset coverage over time without having to recreate their annotation foundation for every change.
Sensor Data Annotation
We label events, conditions, and relevant intervals within time-series data generated by sensors and connected devices. This can support AI applications involving equipment monitoring, industrial systems, predictive analysis, and connected environments. Sensor annotation extends data labeling beyond conventional visual and textual datasets to applications built around machine-generated signals.
Stronger AI Foundations
AI systems learn from the examples available during development, making the underlying dataset an important part of the development foundation. Relevant and accurately labeled examples give teams a clearer basis for developing AI capabilities and reduce the uncertainty that can arise when training data contains unclear or conflicting information during development and model refinement.
Greater Data Usability
Raw data is not always immediately useful for AI development. Annotation adds structure and meaning that allows information to be interpreted according to a defined task. This can make previously difficult-to-use datasets more accessible to development teams and create additional opportunities for applying AI across different organizational functions and operational environments.
More Meaningful Insights
Annotated datasets can help organizations expose patterns, categories, relationships, and characteristics that would otherwise be difficult to analyze consistently. When information is labeled according to meaningful distinctions, AI systems can work with those distinctions more effectively, supporting applications that derive useful info from large and complex datasets across operational scenarios.
Broader AI Applicability
A well-annotated dataset can support different AI initiatives when its information has been structured around meaningful concepts and requirements. This can create opportunities to apply existing data across multiple use cases rather than limiting its value to a single application, particularly when organizations maintain diverse and growing data assets across multiple business functions.
Stronger Development Confidence
AI development involves decisions about models, approaches, and application behavior. Reliable annotated data gives development teams a more dependable basis for making those decisions. When the underlying data is clearly labeled and understood, teams can spend less time questioning the dataset itself and more time addressing the AI problem with greater clarity throughout the development lifecycle.
Greater Value From Existing Data
Organizations often hold substantial amounts of information that remains underused because it is unstructured or difficult to interpret computationally. Annotation can add structure to these existing data assets, creating opportunities to use them within AI initiatives and potentially extending the value of information the organization already possesses without requiring entirely new sources of business data.
Data annotation for AI transformation across industries
Xicom provides data annotation services that help organizations across industries prepare high-quality training data for their AI applications. From computer vision to natural language processing, we label data that reflects your domain, supports your models, and delivers reliable results.
Banking & Finance
Document Annotation for KYC, Transaction Categorization, Fraud Pattern Labeling, Financial Entity Recognition, Compliance Data Tagging
Education
Educational Content Labeling, Student Response Annotation, Learning Material Categorization, Assessment Data Tagging, Curriculum Structure Labeling
Healthcare
Medical Image Annotation, Clinical Documentation Labeling, Patient Record Tagging, Symptom Classification, Treatment Data Structuring
Retail
Product Image Annotation, Customer Review Labeling, Inventory Tagging, Shopping Behavior Categorization, Visual Search Data Labeling
Logistics
Shipment Data Tagging, Route Annotation, Warehouse Location Labeling, Delivery Status Categorization, Supply Chain Event Labeling
Travel & Tourism
Travel Review Labeling, Destination Tagging, Booking Data Annotation, Itinerary Structuring, Customer Feedback Categorization
Automotive
Vehicle Image Annotation, Sensor Data Labeling, Autonomous Driving Tagging, Part Recognition, Quality Inspection Data Labeling
Real Estate
Property Image Annotation, Listing Data Tagging, Location Labeling, Feature Categorization, Tenant Feedback Structuring
Entertainment
Content Tagging, Media Annotation, User Preference Labeling, Recommendation Data Structuring, Audience Engagement Categorization
Manufacturing
Product Defect Annotation, Assembly Line Labeling, Quality Control Tagging, Equipment Data Labeling, Process Event Categorization
Insurance
Claim Document Annotation, Policy Data Labeling, Risk Factor Tagging, Customer Record Structuring, Incident Report Categorization
LET'S BUILD TOGETHER
Accurate Data Annotation for Faster, Reliable Model Training
Our data annotation services help you turn raw data into clean, labeled datasets your models can actually learn from. Scale from pilot to production faster, with fewer errors and less rework along the way.
Data Annotation Engineered for Enterprise Scale
150+
AI Engineers & Data Scientists
300+
AI Solutions Delivered
ISO 9001 Certified
NASSCOM & STPI Accreditation
NASSCOM & STPI Accreditation
500+
Datasets Annotated
30+
Industries Served
Bounding Box Annotation
We use bounding boxes to identify and locate objects within images or video frames. Each relevant object is enclosed within a defined region and assigned the appropriate label according to the annotation requirements. This creates structured datasets for object detection applications across products, equipment, vehicles, people, and other complex visual data types.
Landmark Annotation
We identify predefined reference points within visual data to represent important features or structures. Landmark annotation can be used for facial recognition, object alignment, pose analysis, and other applications requiring precise reference locations. We define landmark categories according to the intended model task and the characteristics of the visual data across different applications.
Polyline Annotation
We use connected lines to mark elongated or continuous features within visual data. This can include roads, lanes, paths, wires, or other linear structures. Polyline annotation provides useful spatial information for applications where identifying the position and direction of continuous features is more appropriate than enclosing them within a box or polygon for precise detection of linear visual elements.
Sentiment Annotation
We label text according to the sentiment or emotional orientation expressed within it, such as positive, negative, or neutral. Depending on the application, more detailed sentiment categories can be defined. This creates labeled datasets for applications that need to understand customer feedback, reviews, or other forms of user-generated text across diverse communication channels and textual datasets.
Polygon Annotation
We use polygon annotation when objects have irregular shapes that cannot be represented accurately through simple rectangular boundaries. Annotators define object contours using multiple points, providing more precise spatial information. This approach can support detailed object recognition, segmentation, and other image analysis requirements where accurate object boundaries are important.
Semantic Segmentation
We annotate images at the pixel level to distinguish specific objects, regions, or visual categories within a scene. This provides detailed information about object boundaries and surrounding areas, supporting applications where precise visual separation matters. Semantic segmentation can be useful for autonomous systems, medical imaging, and industrial computer vision apps.
Instance Segmentation
We label individual objects separately at the pixel level, even when multiple objects belong to the same category. This allows AI models to distinguish between separate instances within an image. We apply instance segmentation where understanding individual objects and their precise boundaries is important for detection, analysis, and visual decision-making across complex visual environments.
Keypoint Annotation
We annotate specific points within images or video to represent meaningful locations, such as body joints, facial landmarks, product features, or mechanical components. These labeled points can support pose estimation, gesture recognition, movement analysis, and other applications where understanding the position and relationship between important visual features is required.
Cuboid Annotation
We use three-dimensional cuboids to represent objects within images or video, capturing their approximate length, width, height, and orientation. This type of annotation provides spatial information beyond two-dimensional boundaries and can support applications involving autonomous systems, robotics, and other scenarios requiring three-dimensional object understanding.
Entity Annotation
We identify and label specific entities within text, such as people, organizations, locations, products, dates, or other defined categories. We apply entity labels according to the requirements of the intended language application, creating structured textual data that can support information extraction, classification, search, and other natural language processing tasks.
Case studies showcasing the value delivered to clients through our solutions
Explore how we partner with clients across industries to deliver tailored AI solutions that improve efficiency, enhance customer experiences, reduce costs, and drive long-term value.
BoxxDocks AI Asset Tracking Platform
The Challenge
Asset tracking was manual, error-prone, and scattered across disconnected systems. The client had no centralized visibility into asset movement, no real-time location data, and no way to generate instant reports across multiple enterprise clients from a single platform.
Our Approach
We built BoxxDocks as an AI-enabled asset lifecycle platform using QR/NFC scanning, real-time dashboards, geolocation tracking, and a RAG-powered conversational engine that lets users query live operational data in plain English instead of navigating dashboards.
The Result
- Real-time inventory tracking across warehouses, transit, and delivery
- AI query engine for natural language insights
- LLM-powered reporting with anomaly detection
- Complete audit trail with location, timestamp, and status
Parity AI AI Finance Platform
The Challenge
Personalized financial advice was expensive and limited to periodic meetings. Users had no way to consolidate banking, investments, debt, and insurance into one platform with real-time, AI-driven guidance.
Our Approach
We built Parity AI as an always-on financial wellness platform powered by AI. It securely pulls data from multiple sources, analyzes user behavior, forecasts outcomes, and delivers personalized recommendations — acting as a digital financial advisor, not a budgeting app.
The Result
- AI-powered financial planning across banking, investments, and debt
- Personalized recommendations based on life events and behavior
- Integrated financial education with actionable insights
IELTS AI-Powered Speaking Evaluator
The Challenge
Human IELTS tutors cost $30-$80/hour with limited availability. Generic speaking apps lack authentic Part 1/2/3 exam structure, pronunciation feedback, and band-aligned scoring rubrics. Learners had no way to get unlimited, exam-authentic practice at scale.
Our Approach
We built a full-stack AI platform with a lifelike AI Avatar Examiner using LangGraph + Gemini conversation engine, Google Cloud TTS with viseme lip-sync, Faster-Whisper ASR, and a 6-layer IELTS evaluation pipeline scoring all 4 criteria with ±0.5-1.0 band accuracy.
The Result
- 1,000+ concurrent users with 2–3s response latency
- 10x lower cost than human examiners
- 6-service AI microservice with model preloading
- No-code admin panel for rapid content updates
Health E Livin AI Wellness Platform
The Challenge
People struggling with mental health had no single platform to access trusted information, take confidential assessments, and find providers — all without stigma or privacy concerns.
Our Approach
We built HealtheLivin as an AI-powered mental wellness app with a conversational AI assistant, anonymous screenings, evidence-based resource library, and geolocation-based provider discovery — all on privacy-first cloud infrastructure.
The Result
- AI knowledgebase for natural language mental health support
- Confidential assessments with personalized wellness insights
- Location-based provider discovery for professional care
- Privacy-first architecture with encrypted data management
Today Fit Fitness App
The Challenge
Gyms, trainers, and users were stuck with disconnected tools for scheduling, payments, and fitness tracking. There was no single platform that combined personalized fitness with business management.
Our Approach
We developed TodayFit as an AI-driven fitness platform with trainer booking, personalized tracking, community features, and a SaaS layer for gym and trainer business management.
The Result
- 30% increase in user engagement
- 4.7+ average rating on the App Store and Play Store
- 60% growth in trainer bookings
- 45% increase in subscription conversions
SmartVend AI Smart Vending Platform
The Challenge
Managing a distributed vending machine network meant manual inventory tracking, disconnected payment systems, no remote management capability, and zero real-time visibility into machine health or sales performance across locations.
Our Approach
We built SmartVend as a cloud-connected IoT platform combining an Android kiosk app, centralized web portal, real-time inventory sync, hardware integration for payments and dispensing, and a digital advertising layer — all managed from a single dashboard.
The Result
- Real-time inventory sync with low-stock alerts
- Remote machine management across unlimited locations
- IoT integration for payments, dispensing, and monitoring
- Centralized sales analytics and revenue reporting
Data annotation technologies we work with
From annotation tools and platforms to the infrastructure that supports large-scale labeling, quality management, and dataset delivery — here's the technology stack we use for enterprise data annotation.
Label Studio
Prodigy
Supervisely
Scale AI
AWS SageMaker
CVAT
Labelbox
VGG Image Annotator
Labelme
SuperAnnotate
brat
Doccano
INCEpTION
tagtog
WebAnno
Amazon S3
Azure Blob
Google Cloud Storage
PostgreSQL
MongoDB
Great Expectations
Cleanlab
Snorkel
Deepchecks
Argilla
AWS
Azure
Google Cloud Platform
DatabricksDomain-aware Teams
Different datasets can require an understanding of the subject matter behind the information being labeled. We align annotation activities with the terminology, concepts, and distinctions relevant to the project, helping teams work more effectively with specialized datasets where accurate interpretation requires familiarity with the underlying domain and the specific context surrounding each dataset.
Dedicated Project Oversight
Large annotation initiatives involve multiple activities, stakeholders, data batches, and delivery milestones. We provide structured project oversight to coordinate these moving parts and maintain visibility into progress. This gives stakeholders a clearer view of the engagement while helping annotation activities remain organized as project scope and volume increase without compromising project visibility.
Flexible Engagement Models
Annotation requirements can vary considerably between projects, from smaller specialized datasets to ongoing high-volume requirements. We can structure our engagement around the nature and scale of the work, allowing enterprises to access annotation capabilities without having to establish and maintain a permanent internal annotation operation while adapting to changing project requirements.
Secure Data Handling
Annotation work may involve proprietary documents, customer information, product data, or other business-sensitive material. We consider appropriate data handling practices throughout the engagement, including access considerations and controlled workflows. This helps organizations manage annotation projects with greater attention to the confidentiality and protection of the underlying data.
Transparent Communication
Annotation projects can evolve as stakeholders review outputs and requirements become clearer. We maintain communication around project progress, observations, issues, and relevant changes, giving stakeholders opportunities to provide input during delivery. This creates a more collaborative engagement and helps reduce misunderstandings as annotation requirements develop.
Capacity To Scale
Enterprise annotation requirements can grow substantially as new datasets, business areas, or applications are introduced. We can support increasing volumes and evolving project demands without requiring organizations to build equivalent internal capabilities from scratch. This gives enterprises greater flexibility when annotation requirements expand beyond the scope of an initial project.
Our comprehensive AI data annotation process
We follow a structured process that moves from understanding data requirements through annotation, quality checks, validation, and dataset delivery for AI development. Each stage is aligned with project specifications, helping maintain consistency, address issues systematically, and produce datasets that are ready for their intended AI workflows and applications.
1
Define Requirements
We identify data types, annotation objectives, labeling scope, categories, and project requirements to establish a clear annotation framework before work begins.
2
Prepare Data
We organize source data, remove unsuitable records, standardize required formats, and prepare files for efficient annotation according to project specifications.
3
Annotate Data
We apply defined labeling methods and guidelines to the selected datasets, capturing relevant information consistently according to the intended AI application.
4
Review Outputs
We examine completed annotations against established requirements, identifying missing labels, incorrect classifications, inconsistencies, and other issues requiring correction before delivery.
5
Deliver Dataset
We finalize validated annotations, organize outputs into required formats, and provide datasets prepared for training, fine-tuning, evaluation, or downstream AI workflows.
Our engagement models for AI data annotation
We offer flexible engagement models for data annotation — fixed-price for well-defined annotation projects, or dedicated teams for ongoing, high-volume annotation requirements across multiple datasets and AI initiatives.
Fixed Price Model
Best for well-defined annotation scopes with clear requirements and deliverables, this model ensures predictable costs and timely delivery without surprises.
- Upfront agreed cost and project scope
- Milestone-based progress tracking
- No hidden charges or overheads
- Reliable delivery timelines and outcomes
Most Popular
Dedicated Teams Model
Ideal for businesses with ongoing annotation needs, this model provides a dedicated team working exclusively on your data labeling, quality management, and dataset preparation.
- Full control over team structure and workflows
- Highly scalable and cost-effective
- Direct communication with annotation teams
- Increased focus and faster turnaround
Time & Material Model
Perfect for annotation projects with evolving requirements, this model offers flexibility to adjust scope, volume, and resources as project needs change.
- Flexible billing based on actual efforts
- Adjust resources and scope anytime
- Ideal for iterative and evolving projects
- Faster implementation and continuous optimization
Compliance we follow in data annotation
Data annotation often involves sensitive information that requires careful handling. We implement security controls, access management, and compliance frameworks including GDPR, HIPAA, SOC 2, and ISO standards to protect your data throughout the annotation lifecycle.
Client testimonials and reviews showcasing the value we consistently deliver
Explore how our clients describe their journey with us, reflecting strong collaboration, effective execution, and consistent outcomes delivered across engagements. See how our delivery framework ensures consistency from initiation through to successful completion.
❝
Parity AI
We came to Xicom with an ambitious goal: make institutional-grade financial planning accessible to everyone through AI. Xicom went beyond the brief, blending conversational AI and predictive analytics into an effortless platform. Parity AI is exactly what we envisioned.
❝
RadioFX
Xicom took the time to understand what our stations and listeners actually need, building something tailored rather than generic. Live chat and polls are now core to how we connect with our audience — the app has become a true extension of our on-air presence.
❝
StudyBuddy
Xicom built exactly what our students needed: a secure space to connect, collaborate, and find tutoring support in one place. Their understanding of student behavior shows in how naturally everything fits together — StudyBuddy is something students genuinely rely on.
❝
BoxxDocks
Xicom helped us bring BoxxDocks to life exactly as we envisioned it. Their team understood the complexity of AI powered real-time asset tracking and delivered a platform that's both powerful and genuinely easy to use.
❝
TodayFit
We are very happy with their services. I cannot recommend them highly enough. The value, ability, and delivery that we have seen has been superb, and we look forward to continuing this relationship.
❝
SmartVend AI
Their team built SmartVend from the ground up, combining hardware integration, real-time inventory sync, and a centralized dashboard that completely changed how we operate. We couldn't have asked for a more capable technical partner.
❝
Tap Retail
Partnering with Xicom to develop our Tap Retail app and website was one of the best decisions we made. Their team demonstrated exceptional professionalism, technical expertise, and a deep understanding of our business goals.
❝
GoGmGo
Partnering with Xicom was a game-changer for us. They didn't just build software, they helped us redefine how we run our business. Their proactive support and deep domain knowledge gave us the confidence to innovate faster.
❝
DENVEN
Working with Xicom has been a transformative experience. Their team quickly understood our challenges and delivered a scalable, intuitive solution. I highly recommend Xicom to any organization seeking future-ready, high-performance digital products.
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Frequently asked questions
What is AI data annotation?
AI data annotation is the process of labeling or tagging data so machine learning models can understand and learn from it. It involves adding metadata, categories, or identifying features to raw data such as text, images, video, audio, and sensor data. Annotation is a foundational step across AI development services, since model accuracy depends directly on how well the training data is labeled.
What types of data can be annotated?
We annotate multiple data types, including text, images, video, audio, sensor data, and time-series data. Our annotation services support computer vision, natural language processing, autonomous systems, and other applications used across healthcare, retail, manufacturing, and beyond.
How do you ensure annotation quality?
We maintain quality through structured processes, including defined labeling guidelines, sampling rates, inter-annotator agreement thresholds, and multi-tier review procedures. This gives us consistent, measurable quality metrics as teams scale to handle larger annotation volumes.
What does data annotation cost?
Data annotation costs depend on factors like data type, task complexity, required precision, data volume, and project timeline. Pricing is typically structured as per-label, per-hour, or per-project, depending on the dataset and workflow involved. Contact our team for a project-specific quote.
Can you handle large-scale annotation projects?
Yes. We scale annotation efforts to handle large datasets and high-volume requirements through flexible engagement models and dedicated teams. This lets organizations support enterprise-level annotation initiatives, and broader AI development services, without building a permanent in-house annotation team.
What security measures do you take with my data?
We follow structured data handling practices aligned with frameworks including GDPR, HIPAA, SOC 2, and ISO 27001, along with access controls, secure workflows, and confidentiality protocols. This helps protect proprietary documents, customer information, and other business-sensitive material throughout the engagement.
Every award marks a milestone in our journey of excellence
As AI-first digital engineering company, Xicom has earned global recognition for delivering innovative, scalable, and high-performing technology solutions. Our awards reflect the trust of clients and industry leaders alike.
